Abstract Socially assistive robots represent a promising tool in assistive contexts for improving people’s quality of life and well-being through social, emotional, cognitive, and physical support. However, the effectiveness of interactions heavily relies on the robots’ ability to adapt to the needs of the assisted individuals and to offer support proactively, before it is explicitly requested. Previous work has primarily focused on defining the actions the robot should perform, rather than considering when to act and how confident it should be in a given situation. To address this gap, this paper introduces a new data-driven framework that involves a learning pipeline, consisting of two phases, with the ultimate goal of training an algorithm based on Influence Diagrams. The proposed assistance scenario involves a sequential memory game, where the robot autonomously learns what assistance to provide when to intervene, and with what confidence to take control. The results from a user study showed that the proactive behaviour of the robot had a positive impact on the users’ game performance. Users obtained higher scores, made fewer mistakes, and requested less assistance from the robot. The study also highlighted the robot’s ability to provide assistance tailored to users’ specific needs and anticipate their requests.

Educational robotics is a field aiming at investigating the use of robots in schools to support teaching and learning activities. While several robotic solutions exist in support of the STEM teaching activities, in this work, we present "Classmate Robot" as a new social robot to be used in the classrooms as a support to the learning experience through interaction. Classmate Robot has been designed and developed to improve the effectiveness of the activities by providing a framework where the robot’s behaviors can be personalized, and learning applications can be easily integrated on top of the robot interaction capabilities. This approach aims to increase the engagement of learners. We introduce the ROS-based architecture developed that is divided into three main layers plus an application layer. As a social robot, it combines several multimodal social cues to interact and communicate with students and teachers. Moreover, the robot is endowed with a set of behaviors designed to be compliant with its role of "classmate" in the interaction with the students.

When modeling the social behavior of a robot, the simulation of a specific personality or different interaction style may affect the perception of the interaction itself and the acceptability of the robot. Different interaction styles may be simulated through the use of verbal and non-verbal features that may not be easily recognized by the user as intended by the designer. For this reason, this study aimed to evaluate how three different robot interaction styles (i.e., Friendly, Neutral, and Authoritarian) were perceived by humans in the context of a robot carrying out cognitive tests. The Self-Assessment Manikin (SAM) was proposed to measure the perceived Valence, Arousal, and Dominance. We expected that a Neutral behavior is characterized by low Arousal, a Friendly by high Valence, and an Authoritarian by high Dominance. Moreover, the perception of a Socially Assistive Robot’s behavior is closely linked to trust, which is a key component to the success of any care-provider/user relationship. Hence, a Trust Perception Scale was used to explore the effect of the interaction style on trust. The results confirmed our hypothesis and showed a significant difference between each value with the others. Furthermore, we expected to obtain a higher value of trust with the Authoritarian since the performance of the users who interacted with the Authoritarian was better than the others. However, this hypothesis was not confirmed by the results.